As AI adoption accelerates, organizations face the challenge of understanding and controlling costs. This framework proposes a hierarchical cost attribution model starting at the token level, aggregating through API calls, features, services, and finally to departments. Key components include tagging every inference request with metadata, building a cost aggregation pipeline, and creating dashboards for different stakeholders. The approach enables data-driven decisions about model selection, caching strategies, and resource allocation. It also supports chargeback models where departments are billed based on actual AI usage, promoting accountability and efficient resource use.
A complete framework for tracking and allocating AI application costs across teams, from individual tokens to departmental budgets.